In this paper, a rapid and quantitative detection method combing the laser Raman spectroscopy with Artificial Bee Colony-Support Vector Machine for Regression (ABC-SVR) is proposed for the determination of fatty acids content in three-component blend oil. This method establishes a mathematical model with higher prediction accuracy and higher modeling efficiency than similar comparison algorithms in solving the nonlinear and high-dimensional complex relationship between spectral data information and samples. And it can avoid complicated detection ways like gas chromatography and liquid chromatography, etc. The quality of fatty acids is obtained from the oil configuration volume according to the international standards for the content of three fatty acids in pure oils, which effectively reduces the cost and complexity of the experiment, and increases the practical value of the inspection work. Firstly, 66 groups of mixed oil test samples were arranged according to a certain gradient. The Raman spectroscopic information of the samples was collected from a portable Raman spectrometer and the background noise was subtracted at the time. Through the spectrum of the samples, we could see that the Raman spectra had the same characteristic peak shifts basically, but the intensities of the characteristic peaks were obviously different because of the difference in functional group concentration. Therefore, different components could be distinguished according to the characteristic peak information. Secondly, the spectra were pretreated by background subtraction, spectral smoothing and normalization to reduce the effect of uncontrollable external factors in the experiment. Then the mass of fatty acid was obtained from the oil volume by the international standard content of three kinds of fatty acids in pure oil in National Codex Alimentarius Commission Standard CODEX STAN210—1999.2/3 of sample data were randomly selected as the training set, and the remaining 1/3 of sample data were used as the prediction set. The characteristic peak intensity and the quality of fatty acid of train set were used as the input and output values of the regression model, and the quantitative analysis model of hybrid optimization algorithms of SVR, PSO-SVR and ABC-SVR were established to predict the content of fatty acids of test set. The accuracy of the model was tested by using mean squared error (MSE), the correlation coefficient (R2) and elapsed time. The experimental results showed that the ABC-SVR quantitative analysis model was effective: the MSE of the predicted and true values of three fatty acid contents were 0.88×10-4, 16×10-4 and 8×10-4, respectively. The R2 were 93.43%, 99.65% and 99.43%, respectively. The elapsed time were 1.26, 2.42 and 2.14 s, respectively. Therefore, the proposed method has higher accuracy, faster modeling time than other ways, and it can be applied to other sample detection work under theoretically similar conditions. This method can provide a viable theoretical basis for further study on the analysis of adulterated edible oils by vibration spectroscopy.